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Distribution models for mountain plant species: The value of elevation

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  • Oke, Oluwatobi A.
  • Thompson, Ken A.

Abstract

The climatic conditions of mountain habitats are greatly influenced by topography. Large differences in microclimate occur with small changes in elevation, and this complex interaction is an important determinant of mountain plant distributions. In spite of this, elevation is not often considered as a relevant predictor in species distribution models (SDMs) for mountain plants. Here, we evaluated the importance of including elevation as a predictor in SDMs for mountain plant species. We generated two sets of SDMs for each of 73 plant species that occur in the Pacific Northwest of North America; one set of models included elevation as a predictor variable and the other set did not. AUC scores indicated that omitting elevation as a predictor resulted in a negligible reduction of model performance. However, further analysis revealed that the omission of elevation resulted in large over-predictions of species’ niche breadths—this effect was most pronounced for species that occupy the highest elevations. In addition, the inclusion of elevation as a predictor constrained the effects of other predictors that superficially affected the outcome of the models generated without elevation. Our results demonstrate that the inclusion of elevation as a predictor variable improves the quality of SDMs for high-elevation plant species. Because of the negligible AUC score penalty for over-predicting niche breadth, our results support the notion that AUC scores alone should not be used as a measure of model quality. More generally, our results illustrate the importance of selecting biologically relevant predictor variables when constructing SDMs.

Suggested Citation

  • Oke, Oluwatobi A. & Thompson, Ken A., 2015. "Distribution models for mountain plant species: The value of elevation," Ecological Modelling, Elsevier, vol. 301(C), pages 72-77.
  • Handle: RePEc:eee:ecomod:v:301:y:2015:i:c:p:72-77
    DOI: 10.1016/j.ecolmodel.2015.01.019
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    References listed on IDEAS

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    1. Hof, Anouschka R. & Jansson, Roland & Nilsson, Christer, 2012. "The usefulness of elevation as a predictor variable in species distribution modelling," Ecological Modelling, Elsevier, vol. 246(C), pages 86-90.
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    Cited by:

    1. Kosicki, Jakub Z., 2017. "Should topographic metrics be considered when predicting species density of birds on a large geographical scale? A case of Random Forest approach," Ecological Modelling, Elsevier, vol. 349(C), pages 76-85.
    2. Sangui Yi & Jihua Zhou & Liming Lai & Qinglin Sun & Xin Liu & Benben Liu & Jiaojiao Guo & Yuanrun Zheng, 2021. "Different Causal Factors Occur between Land Use/Cover and Vegetation Classification Systems but Not between Vegetation Classification Levels in the Highly Disturbed Jing-Jin-Ji Region of China," Sustainability, MDPI, vol. 13(8), pages 1-23, April.
    3. L. Lombardo & G. Fubelli & G. Amato & M. Bonasera, 2016. "Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 565-588, October.
    4. Kim, Seokmin & Koop, Anthony & Fowler, Glenn & Israel, Kimberly & Takeuchi, Yu & Lieurance, Deah, 2023. "Addition of finer scale data and uncertainty analysis increases precision of geospatial suitability model for non-native plants in the US," Ecological Modelling, Elsevier, vol. 484(C).
    5. Ji-Zhong Wan & Chun-Jing Wang & Fei-Hai Yu, 2017. "Spatial conservation prioritization for dominant tree species of Chinese forest communities under climate change," Climatic Change, Springer, vol. 144(2), pages 303-316, September.

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